Electrocardiography versus photoplethysmography in assessment of maternal heart rate variability during labor

Computerized analysis of cardiotocograms in clinical practice and the SisPorto® system thirty-two years after: technological, physiopathological and clinical studies

OBJECTIVES

The objective of this study is to present the why, what and how about computerized analysis of cardiotocograms (cCTG) and the SisPorto system for cCTG.

CONTENT

A narrative review about cCTG and the SisPorto system for cCTG is presented. The meta-analysis of randomized controlled trials (RCT) performed so far have evidenced that cCGT compared to traditional CTG analysis may save time spent in hospital for women, in the antepartum period, and is objective with at least equivalent results in maternal and perinatal outcomes, both in the ante and intrapartum periods. The SisPorto system for cCTG closely follows the FIGO guidelines for fetal monitoring. It may be used both in the ante and intrapartum periods, alone or connected to a central monitoring station, with simultaneous monitoring of fetal and maternal signals, not only in singletons but also in twins. It has been assessed in technical, physiopathological and clinical studies, namely in one large multicentric international RCT during labor and two meta-analysis.

SUMMARY AND OUTLOOK

There is evidence that cCTG may be useful in clinical practice with advantages compared to traditional CTG analysis, although without clear impact on the decrease of preventable maternal and perinatal mortality and morbidity. More studies are warranted, namely on technical improvements and assessment in larger studies in a wider range of clinical settings.

Quantitative cardiac autonomic outcomes of hydrotherapy in women during the first stage of labor

Introduction

Most hydrotherapy studies during childbirth report findings related to pain using a widespread set of subjective measures. In this study, ECG biomarkers as quantitative cardiac autonomic outcomes were used to assess the effects of warm shower hydrotherapy on laboring women during the first stage of labor.

Methods

This was a prospective single-blind cohort study on stage I delivering women. Their cardiac autonomic function was assessed using heart rate variability (HRV) measures during a deep breathing test using point-of-care testing comprised of an HRV scanner system with wireless ECG enabling real-time data analysis and visualization. Labor pain and anxiety were assessed using the Visual Analog Scale for Pain (VASP) and the Beck Anxiety Inventory (BAI). A total of 105 pregnant women in the first stage of labor who received warm shower hydrotherapy, intravenous analgesia (scopolamine + sodium dipyrone), or spinal anesthetic (bupivacaine + morphine) were enrolled.

Results

In women during the first stage of labor, parasympathetic modulation reflected through RMSSD (root mean square of successive RR interval differences) was significantly reduced by hydrotherapy and intravenous analgesia (before vs. after mean rank diff. 35.73 and 65.93, respectively, p < 0.05). Overall HRV (SDNN, standard deviation of RR intervals) was significantly decreased only by intravenous analgesia (before vs. after mean rank diff. 65.43, p < 0.001). Mean heart rate was significantly increased by intravenous analgesia, while spinal anesthesia reduced it, and hydrotherapy did not alter it (before vs. after mean rank diff. -49.35*, 70.38*, -24.20 ^NS , respectively, *p < 0.05, ^NS not significant).

Conclusion

This study demonstrates that warm shower therapy may impact the sympathovagal balance via parasympathetic withdrawal in women during the initial stage of labor. The findings of this study provide quantitative support for using warm shower hydrotherapy during labor via point-of-care testing. The dependability of hydrotherapy as a non-pharmacological treatment is linked to the completion of more clinical research demonstrating quantitative evidence via outcome biomarkers to support indications on stress and birth progress.

Comparison of Apple Watch Series 4 vs. KardiaMobile: A Tale of Two Devices

Background

The Apple Watch Series 4 (AW4) and the KardiaMobile single bipolar lead model (KM) are 2 of the most popular US Food & Drug Administration (FDA)-approved commercial heart trackers. However, a lack of knowledge remains regarding their rhythm-detection accuracy in real-life clinical situations. This paper aims to determine the practicality of using an AW4 or a KM in modern medical practice, by assessing the accuracy of each in identifying heart rhythms and heart rate.

Methods

Participants from the Toronto Heart Centre clinic were enrolled from January 2019 to December 2019. They had a 12-lead electrocardiogram (ECG), followed by wearing the AW4 watch (OS 5.3), and pressing on the KM electrode plates, within the span of 5 minutes of one another. Each session involved a 12-lead ECG, an ECG from each device, and AW4’s photoplethysmography function (APPG).

Results

Of 200 participants, 162 (81%) were in sinus rhythm, and 38 (19%) had atrial fibrillation. The rhythm-detection accuracy for sinus rhythm was 100% for the AW4, and 99.03% for the KM. For atrial fibrillation, accuracy was 90.48% for the AW4, and 100% for the KM. The heart rate accuracy for sinus rhythm was 94.39% for the KM, 90.65% for the APPG, and 96.26% for the Apple ECG function. The heart rate accuracy for atrial fibrillation was 91.30% for the KM, 82.61% for the APPG, and 86.96% for the Apple ECG function.

Conclusions

Both the AW4 and the KM could reliably detect rhythm and heart rate in real-life clinical situations. However, a nonsignificant trend occurred toward better rhythm detection and accuracy with KM, compared with AW4. The difference is mainly due to artifacts (eg, tremors) and the fit of the strap for AW4. The findings have important implications for how these consumer devices can be used in real-life clinical settings.

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning

In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about DL's utility in non-invasive biometric monitoring during pregnancy. A recently established self-supervised learning (SSL) approach to DL provides emotional recognition from electrocardiogram (ECG). We hypothesized that SSL will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG. Chronically stressed mothers and controls matched at enrolment at 32 weeks of gestation were studied. We validated the chronic stress exposure by psychological inventory, maternal hair cortisol and FSI. We tested two variants of SSL architecture, one trained on the generic ECG features for emotional recognition obtained from public datasets and another transfer-learned on a subset of our data. Our DL models accurately detect the chronic stress exposure group (AUROC = 0.982 ± 0.002), the individual psychological stress score (R2 = 0.943 ± 0.009) and FSI at 34 weeks of gestation (R2 = 0.946 ± 0.013), as well as the maternal hair cortisol at birth reflecting chronic stress exposure (0.931 ± 0.006). The best performance was achieved with the DL model trained on the public dataset and using maternal ECG alone. The present DL approach provides a novel source of physiological insights into complex multi-modal relationships between different regulatory systems exposed to chronic stress. The final DL model can be deployed in low-cost regular ECG biosensors as a simple, ubiquitous early stress detection and monitoring tool during pregnancy. This discovery should enable early behavioral interventions.

Heart rate variability and hematological parameters in pregnant women

Background

There are few researches on hematological parameters (hemoglobin, red cell distribution width [RDW], white blood cells [WBCs], mean platelets volume [MPV], and heart rate variability [HRV]). There are no published data on this concept (HRV and hematological parameters) during pregnancy.

Methods

A cross‐sectional study was conducted at Saad Abul Ela hospital in Khartoum, Sudan during the period of July to August 2018. Pregnant women with singleton, a live baby, were enrolled in this study. Clinical history and examination were performed. HRV (autonomic modulation) was assessed using time and frequency domain HRV indices.

Results

One hundred and five pregnant women were enrolled. The median (quartile) of the age, parity, and gestational age was 30.0 (25.0‐35.0) years, 1.0 (0‐3.0), and 38.0 (32.0‐39.0) weeks, respectively. While there were positive correlations between hemoglobin and low frequency (LF), RDW and high frequency (HF), WBCs and HF Norm, WBCs and LF/HF, MPV and HF Norm, LF Norm and LF/HF, there was no significant correlation between the hematological (hemoglobin, WBCs, RDW, and MPV) and HRV parameters. Linear regression analysis showed no significant association between age, parity, gestational age, body mass index, hemoglobin, RDW, and HRV variables. The Log₁₀ WBCs were negatively associated with Log₁₀ HF (ms²/Hz). MPV was positively associated with LF Norm and negatively associated with HF Norm.

Conclusion

The study failed to show significant associations between age, parity, gestational age, hemoglobin, RDW, and HRV variables. The WBCs were negatively associated with HF. MPV was positively associated with LF Norm, and it was negatively associated with HF Norm.

A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.

Surface electromyography for analysis of heart rate variability in preterm infants

OBJECTIVE

Characterizing heart rate variability (HRV) in neonates has gained increased attention and is helpful in quantifying maturation and risk of sepsis in preterm infants. Raw data used to derive HRV in a clinical setting commonly contain noise from motion artifacts. Thoracic surface electromyography (sEMG) potentially allows for pre-emptive removal of motion artifacts and subsequent detection of interbeat interval (IBI) of heart rate to calculate HRV. We tested the feasibility of sEMG in preterm infants to exclude noisy raw data and to derive IBI for HRV analysis. We hypothesized that a stepwise quality control algorithm can identify motion artifacts which influence IBI values, their distribution in the time domain, and outcomes of nonlinear time series analysis.

APPROACH

This is a prospective observational study in preterm infants  <6 days of age. We used 100 sEMG measurements from 24 infants to develop a semi-automatic quality control algorithm including synchronized video recording, threshold-based sEMG envelope curve, optimized QRS-complex detection, and final targeted visual inspection of raw data.

MAIN RESULTS

Analysis of HRV from sEMG data in preterm infants is feasible. A stepwise algorithm to exclude motion artifacts and improve QRS detection significantly influenced data quality (34% of raw data excluded), distribution of IBI values in the time domain, and nonlinear time series analysis. The majority of unsuitable data (94%) were excluded by automated steps of the algorithm.

SIGNIFICANCE

Thoracic sEMG is a promising method to assess motion artifacts and calculate HRV in preterm neonates.

Maternal Heart Rate Variability during the First Stage of Labor

Labor necessitates continuous adjustments of cardiac autonomic reflexes by alternate activation of the sympathetic and parasympathetic nervous systems. The division of the autonomic nervous system (ANS) that predominates during the first stage of labor is unclear and needs to be further investigated. The study aimed to compare heart rate variability (HRV) in pregnant women in the third trimester with those during the first stage of labor. We conducted a case-control study at Saad Abul Ela Maternity Hospital, Khartoum, Sudan. Forty-five women with singleton, live neonates in the first stage of labor and 45 women in the third trimester (but not in labor) were enrolled as case and control groups, respectively. Data on the medical history, obstetrics history, and clinical examinations that were performed in all of the studied women were obtained using prearranged questionnaires. Cardiac autonomic modulation (CAM) of the heart was examined in both groups based on time and frequency domain HRV indices. There were no significant differences in age, parity, body mass index, and hemoglobin levels between the two groups. Pregnant women in labor had significantly higher LnSDNN, LnRMSSD, LnTP, LnVLF, LnLF, LnHF, LF Norm, and LnLF/HF ratio, but lower HF Norm compared with controls (P < 0.001). These findings remained unchanged when possible confounders were controlled for using regression analysis. Our findings suggest a significant increase in indictors of sympathetic CAM, namely LF Norm and LnLF/HF, during labor. Sympathetic hypertonia associated with labor is unlikely to increase the risk of cardiac events because sympathetic CAM simultaneously increases with global HRV. Increased HRV during labor may be explained by parasympathetic activation as indicated by higher LnHF and LnRMSSD at the time of delivery.